Protection of your social media accounts
Introduction
In the interconnected world of social networking and the digital landscape, social media users have faced some issues like hacking. Hence there is a necessity to protect your personal information and data from scammers or hackers. In case your email or social media account gets hacked, there are mechanisms or steps you can utilise to recover your email or social media account. It is important to protect your email or social media accounts in order to protect your personal information and data on your account. It is always advisable to keep strong passwords to protect your account and enable two-factor authentication as an extra layer of protection. Hackers or bad actors can take control of your account, they can even change the linked mail ID or Mobile numbers to take full access to your account.
Recent Incident
Recently, a US man's Facebook account was deleted or disabled by Facebook. He has sued Facebook and initiated a legal battle. He has contended that there was no violation of any terms and policy of the platform, and his account was disabled. In the first instance, he approached the platform. However, the platform neglected his issue then he filed a suit, where the court ordered Facebook's parent company, Meta, to pay $50,000 compensation, citing ignorance of the tech company.
Social media account recovery using the ‘Help’ Section
If your Facebook account has been disabled, when you log in to your account, you will see a text saying that your account is disabled. If you think that your account is disabled by mistake, in such a scenario, you can make a request to Facebook to ‘review’ its decision using the help centre section of the platform. To recover your social media account, you can go to the “Help” section of the platform where you can fix a login problem and also report any suspicious activity you have faced in your account.
Best practices to stay protected
- Strong password: Use strong and unique passwords for your email and all social media accounts.
- Privacy settings: You can utilise the privacy settings of the social media platform, where you can set privacy as to who can see your posts and who can see your contact information, and you can also keep your social media account private. You might have noticed a few accounts on which the user's name is unusual and isn’t one which you recognise. The account has few or no friends, posts, or visible account activity.
- Avoid adding unknown users or strangers to your social networking accounts: Unknown users might be scammers who can steal your personal information from your social media profiles, and such bad actors can misuse that information to hack into your social media account.
- Report spam accounts or posts: If you encounter any spam post, spam account or inappropriate content, you can report such profile or post to the platform using the reporting centre. The platform will review the report and if it goes against the community guidelines or policy of the platform. Hence, recognise and report spam, inappropriate, and abusive content.
- Be cautious of phishing scams: As a user, we encounter phishing emails or links, and phishing attacks can take place on social media as well. Hence, it is important that do not open any suspicious emails or links. On social media, ‘Quiz posts’ or ‘advertisement links’ may also contain phishing links, hence, do not open or click on such unauthenticated or suspicious links.
Conclusion
We all use social media for connecting with people, sharing thoughts, and lots of other activities. For marketing or business, we use social media pages. Social media offers a convenient way to connect with a larger community. We also share our personal information on the platform. It becomes important to protect your personal information, your email and all your social media accounts from hackers or bad actors. Follow the best practices to stay safe, such as using strong passwords, two-factor authentication, etc. Hence contributing to keeping your social media accounts safe and secure.
References:
- https://www.gpb.org/news/2023/07/11/facebook-wrongfully-deleted-his-account-he-sued-the-company-and-won
- https://consumer.ftc.gov/articles/how-recover-your-hacked-email-or-social-media-account
Related Blogs
Executive Summary:
Amid the ongoing conflict involving the United States, Israel, and Iran, a video showing a building engulfed in flames is being widely circulated on social media. In the clip, a large fire can be seen inside a building while several people appear to be running in panic. The video is being shared with the claim that Iran fired a hypersonic missile targeting a ceremony in Tel Aviv, Israel, allegedly killing several Israeli military generals and other prominent figures.
However, research by the CyberPeace found that the claim is false. The video being circulated as footage of an attack in Israel actually predates the current conflict and shows a fire that broke out during a wedding ceremony.
Claim
A Facebook user named “Syed Asif Raza Jafri” shared the video on March 13, 2026, claiming that an Iranian hypersonic missile had struck a grand ceremony in Tel Aviv, where several Israeli military officers, generals, soldiers, and other important personalities were present. According to the post, the attack resulted in multiple casualties.
Source:
- https://www.facebook.com/reel/902182825912364
- https://ghostarchive.org/archive/rZryr
Fact Check
To verify the claim, we began our research using the Google Lens reverse image search tool. Several key frames from the viral video were extracted and searched online.
During the search, we found the same video shared earlier on multiple foreign social media accounts. A Facebook user named “Es de Bombero” from Chile had posted the video on January 17, 2026, describing it in Spanish as footage of a fire that broke out during a wedding celebration.
Our research shows that the viral video had been circulating on social media since at least January 15, 2026, well before the escalation of the current conflict. According to a report published on March 1, 2026, by BBC, the large-scale attacks on Iran by the United States and Israel began on February 28, 2026, after which Iran’s Supreme Leader Ali Khamenei was reported dead.
Additionally, a March 12, 2026 report by Al Jazeera stated that a house near Tel Aviv in central Israel was damaged by a rocket reportedly fired by Hezbollah, which has previously carried out joint attacks in coordination with Iran.
Conclusion
The viral video being shared as footage of an Iranian hypersonic missile strike in Tel Aviv is misleading. The clip is an older video of a fire that reportedly broke out during a wedding ceremony and was circulating online before the current conflict began.
While the exact location of the incident shown in the video cannot be independently verified, it is clear that the footage has no connection to the ongoing war between the United States, Israel, and Iran.
Executive Summary:
A viral online video claims of an attack on Prime Minister Benjamin Netanyahu in the Israeli Senate. However, the CyberPeace Research Team has confirmed that the video is fake, created using video editing tools to manipulate the true essence of the original footage by merging two very different videos as one and making false claims. The original footage has no connection to an attack on Mr. Netanyahu. The claim that endorses the same is therefore false and misleading.
Claims:
A viral video claims an attack on Prime Minister Benjamin Netanyahu in the Israeli Senate.
Fact Check:
Upon receiving the viral posts, we conducted a Reverse Image search on the keyframes of the video. The search led us to various legitimate sources featuring an attack on an ethnic Turkish leader of Bulgaria but not on the Prime Minister Benjamin Netanyahu, none of which included any attacks on him.
We used AI detection tools, such as TrueMedia.org, to analyze the video. The analysis confirmed with 68.0% confidence that the video was an editing. The tools identified "substantial evidence of manipulation," particularly in the change of graphics quality of the footage and the breakage of the flow in footage with the change in overall background environment.
Additionally, an extensive review of official statements from the Knesset revealed no mention of any such incident taking place. No credible reports were found linking the Israeli PM to the same, further confirming the video’s inauthenticity.
Conclusion:
The viral video claiming of an attack on Prime Minister Netanyahu is an old video that has been edited. The research using various AI detection tools confirms that the video is manipulated using edited footage. Additionally, there is no information in any official sources. Thus, the CyberPeace Research Team confirms that the video was manipulated using video editing technology, making the claim false and misleading.
- Claim: Attack on the Prime Minister Netanyahu Israeli Senate
- Claimed on: Facebook, Instagram and X(Formerly Twitter)
- Fact Check: False & Misleading
The Expanding Governance Challenge of Artificial Intelligence
Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk.
Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.
The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process.
This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
The Lifecycle of an AI System
AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.
The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.
Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.
Where AI Risks Emerge
AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks: bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.
AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence
Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms.
AI System Lifecycle: Key Risks at Each Stage
Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
Policy Tools for Mitigating AI Risks
Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment.
For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.
Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development.
Governance Overlay: Policy Interventions Across the AI Lifecycle
Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
The Policy Gap in AI Governance
The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.
An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment.
Analysis
1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.
2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment.
3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems.
4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
Recommendations
1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.
2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.
3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.
4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
Conclusion - The Need for Global AI Governance
Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.
Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
References
- OECD AI lifecycle
- OECD AI system lifecycle description
- OECD AI governance lifecycle framework
- EU AI Act overview
- EU AI Act risk categories
- UNESCO Recommendation on the Ethics of AI
- AI governance lifecycle analysis
- OECD AI policy tools database
